Anomalous sign-in location by user account and authenticating application

// Adjust this figure to adjust how sensitive this detection is
let sensitivity = 2.5;
let AuthEvents = materialize(
union isfuzzy=True SigninLogs, AADNonInteractiveUserSignInLogs
| where TimeGenerated > ago(7d)
| where ResultType == 0
| extend LocationDetails = LocationDetails_dynamic
| extend Location = strcat(LocationDetails.countryOrRegion, "-", LocationDetails.state,"-", LocationDetails.city)
| where Location != "--");
AuthEvents
| summarize dcount(Location) by AppDisplayName, AppId, UserPrincipalName, UserId, bin(startofday(TimeGenerated), 1d)
| where dcount_Location > 2
| make-series CountOfLocations = sum(dcount_Location) on TimeGenerated  step 1d by AppId, UserId
| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfLocations, sensitivity, -1, 'linefit')
| mv-expand CountOfLocations to typeof(double), TimeGenerated to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)
| where Anomalies > 0 and Baseline > 0
| join kind=inner( AuthEvents | extend TimeStamp = startofday(TimeGenerated)) on UserId, AppId
| extend SignInDetails = bag_pack("TimeGenerated", TimeGenerated1, "Location", Location, "Source", IPAddress, "Device", DeviceDetail_dynamic)
| summarize SignInDetailsSet=make_set(SignInDetails, 1000) by UserId, UserPrincipalName, CountOfLocations, TimeGenerated, AppId, AppDisplayName
| extend Name = split(UserPrincipalName, "@")[0], UPNSuffix = split(UserPrincipalName, "@")[1]

kind: Scheduled
queryPeriod: 7d
customDetails:
  Application: AppDisplayName
description: |
  'This query over Microsoft Entra ID sign-in considers all user sign-ins for each Microsoft Entra ID application and picks out the most anomalous change in location profile for a user within an
  individual application.  
tactics:
- InitialAccess
id: 7cb8f77d-c52f-4e46-b82f-3cf2e106224a
requiredDataConnectors:
- connectorId: AzureActiveDirectory
  dataTypes:
  - SigninLogs
- connectorId: AzureActiveDirectory
  dataTypes:
  - AADNonInteractiveUserSignInLogs
relevantTechniques:
- T1078
severity: Medium
version: 2.0.3
status: Available
entityMappings:
- entityType: Account
  fieldMappings:
  - identifier: FullName
    columnName: UserPrincipalName
  - identifier: Name
    columnName: Name
  - identifier: UPNSuffix
    columnName: UPNSuffix
- entityType: Account
  fieldMappings:
  - identifier: AadUserId
    columnName: UserId
name: Anomalous sign-in location by user account and authenticating application
triggerOperator: gt
eventGroupingSettings:
  aggregationKind: SingleAlert
query: |
  // Adjust this figure to adjust how sensitive this detection is
  let sensitivity = 2.5;
  let AuthEvents = materialize(
  union isfuzzy=True SigninLogs, AADNonInteractiveUserSignInLogs
  | where TimeGenerated > ago(7d)
  | where ResultType == 0
  | extend LocationDetails = LocationDetails_dynamic
  | extend Location = strcat(LocationDetails.countryOrRegion, "-", LocationDetails.state,"-", LocationDetails.city)
  | where Location != "--");
  AuthEvents
  | summarize dcount(Location) by AppDisplayName, AppId, UserPrincipalName, UserId, bin(startofday(TimeGenerated), 1d)
  | where dcount_Location > 2
  | make-series CountOfLocations = sum(dcount_Location) on TimeGenerated  step 1d by AppId, UserId
  | extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfLocations, sensitivity, -1, 'linefit')
  | mv-expand CountOfLocations to typeof(double), TimeGenerated to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)
  | where Anomalies > 0 and Baseline > 0
  | join kind=inner( AuthEvents | extend TimeStamp = startofday(TimeGenerated)) on UserId, AppId
  | extend SignInDetails = bag_pack("TimeGenerated", TimeGenerated1, "Location", Location, "Source", IPAddress, "Device", DeviceDetail_dynamic)
  | summarize SignInDetailsSet=make_set(SignInDetails, 1000) by UserId, UserPrincipalName, CountOfLocations, TimeGenerated, AppId, AppDisplayName
  | extend Name = split(UserPrincipalName, "@")[0], UPNSuffix = split(UserPrincipalName, "@")[1]  
queryFrequency: 1d
alertDetailsOverride:
  alertDescriptionFormat: |
    This query over Microsoft Entra ID sign-in considers all user sign-ins for each Microsoft Entra ID application and picks out the most anomalous change in location profile for a user within an
    individual application. This has detected {{UserPrincipalName}} signing into {{AppDisplayName}} from {{CountOfLocations}} 
    different locations.    
  alertDisplayNameFormat: Anomalous sign-in location by {{UserPrincipalName}} to {{AppDisplayName}}
OriginalUri: https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Microsoft Entra ID/Analytic Rules/AnomalousUserAppSigninLocationIncrease-detection.yaml
triggerThreshold: 0

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "workspace": {
      "type": "String"
    }
  },
  "resources": [
    {
      "apiVersion": "2023-02-01-preview",
      "id": "[concat(resourceId('Microsoft.OperationalInsights/workspaces/providers', parameters('workspace'), 'Microsoft.SecurityInsights'),'/alertRules/7cb8f77d-c52f-4e46-b82f-3cf2e106224a')]",
      "kind": "Scheduled",
      "name": "[concat(parameters('workspace'),'/Microsoft.SecurityInsights/7cb8f77d-c52f-4e46-b82f-3cf2e106224a')]",
      "properties": {
        "alertDetailsOverride": {
          "alertDescriptionFormat": "This query over Microsoft Entra ID sign-in considers all user sign-ins for each Microsoft Entra ID application and picks out the most anomalous change in location profile for a user within an\nindividual application. This has detected {{UserPrincipalName}} signing into {{AppDisplayName}} from {{CountOfLocations}} \ndifferent locations.\n",
          "alertDisplayNameFormat": "Anomalous sign-in location by {{UserPrincipalName}} to {{AppDisplayName}}"
        },
        "alertRuleTemplateName": "7cb8f77d-c52f-4e46-b82f-3cf2e106224a",
        "customDetails": {
          "Application": "AppDisplayName"
        },
        "description": "'This query over Microsoft Entra ID sign-in considers all user sign-ins for each Microsoft Entra ID application and picks out the most anomalous change in location profile for a user within an\nindividual application.\n",
        "displayName": "Anomalous sign-in location by user account and authenticating application",
        "enabled": true,
        "entityMappings": [
          {
            "entityType": "Account",
            "fieldMappings": [
              {
                "columnName": "UserPrincipalName",
                "identifier": "FullName"
              },
              {
                "columnName": "Name",
                "identifier": "Name"
              },
              {
                "columnName": "UPNSuffix",
                "identifier": "UPNSuffix"
              }
            ]
          },
          {
            "entityType": "Account",
            "fieldMappings": [
              {
                "columnName": "UserId",
                "identifier": "AadUserId"
              }
            ]
          }
        ],
        "eventGroupingSettings": {
          "aggregationKind": "SingleAlert"
        },
        "OriginalUri": "https://github.com/Azure/Azure-Sentinel/blob/master/Solutions/Microsoft Entra ID/Analytic Rules/AnomalousUserAppSigninLocationIncrease-detection.yaml",
        "query": "// Adjust this figure to adjust how sensitive this detection is\nlet sensitivity = 2.5;\nlet AuthEvents = materialize(\nunion isfuzzy=True SigninLogs, AADNonInteractiveUserSignInLogs\n| where TimeGenerated > ago(7d)\n| where ResultType == 0\n| extend LocationDetails = LocationDetails_dynamic\n| extend Location = strcat(LocationDetails.countryOrRegion, \"-\", LocationDetails.state,\"-\", LocationDetails.city)\n| where Location != \"--\");\nAuthEvents\n| summarize dcount(Location) by AppDisplayName, AppId, UserPrincipalName, UserId, bin(startofday(TimeGenerated), 1d)\n| where dcount_Location > 2\n| make-series CountOfLocations = sum(dcount_Location) on TimeGenerated  step 1d by AppId, UserId\n| extend (Anomalies, Score, Baseline) = series_decompose_anomalies(CountOfLocations, sensitivity, -1, 'linefit')\n| mv-expand CountOfLocations to typeof(double), TimeGenerated to typeof(datetime), Anomalies to typeof(double), Score to typeof(double), Baseline to typeof(long)\n| where Anomalies > 0 and Baseline > 0\n| join kind=inner( AuthEvents | extend TimeStamp = startofday(TimeGenerated)) on UserId, AppId\n| extend SignInDetails = bag_pack(\"TimeGenerated\", TimeGenerated1, \"Location\", Location, \"Source\", IPAddress, \"Device\", DeviceDetail_dynamic)\n| summarize SignInDetailsSet=make_set(SignInDetails, 1000) by UserId, UserPrincipalName, CountOfLocations, TimeGenerated, AppId, AppDisplayName\n| extend Name = split(UserPrincipalName, \"@\")[0], UPNSuffix = split(UserPrincipalName, \"@\")[1]\n",
        "queryFrequency": "P1D",
        "queryPeriod": "P7D",
        "severity": "Medium",
        "status": "Available",
        "suppressionDuration": "PT1H",
        "suppressionEnabled": false,
        "tactics": [
          "InitialAccess"
        ],
        "techniques": [
          "T1078"
        ],
        "templateVersion": "2.0.3",
        "triggerOperator": "GreaterThan",
        "triggerThreshold": 0
      },
      "type": "Microsoft.OperationalInsights/workspaces/providers/alertRules"
    }
  ]
}